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| Hauptseite > Workflowsammlungen > In Bearbeitung > High Spatial Resolution Imaging of Solar-induced Chlorophyll Fluorescence (SIF) from an Uncrewed Aerial Vehicle (UAV): Development and Evaluation of Image-processing Workflows |
| Hauptseite > Workflowsammlungen > In Bearbeitung > High Spatial Resolution Imaging of Solar-induced Chlorophyll Fluorescence (SIF) from an Uncrewed Aerial Vehicle (UAV): Development and Evaluation of Image-processing Workflows |
| Journal Article | FZJ-2026-03811 |
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2026
IEEE
New York, NY
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Please use a persistent id in citations: doi:10.1109/JSTARS.2026.3717082
Abstract: Remote sensing of solar-induced chlorophyll fluorescence (SIF) is rapidly developing to track vegetation’s photosynthetic activity and related stress from the leaf to the ecosystem level at scales ranging from in-situ measurements to satellite products. Airborne sensors, such as HyPlant, are promising tools for measuring the SIF of larger areas. However, airborne data have limited spatial resolution (in the range of 1 m), and data acquisition is relatively expensive. To bridge the gap between proximal and airborne observations, the previously reportedUAV-based SIFcam system enables field-scale mapping of far-red fluorescence (F760) at centimetre spatial resolution. This study introduces two options for an end-to-end processing chain, followed by a quality assurance framework. Two distinct processing workflows were outlined: (i) a Structure-from-Motion (SfM) photogrammetric approach implemented in Agisoft Metashape (Wf1) and (ii) a custom MATLAB-based image mosaicking pipeline (Wf2). The performance of SIFcam-derived mosaics were assessed through geometric accuracy metrics, inter-workflow radiometric comparisons, and background noise analysis using ground targets, while SIF retrieval accuracy was evaluated through cross-comparisons with reference measurements from the mobile ground-based FloX system and airborne HyPlant imagery. Wf1 achieved highest geometric accuracy, with a lower alignment error (0.46 vs. 1.46 pixels) and markedly higher tie point redundancy (average multiplicity 28.09 vs. 4.13), resulting in more consistent mosaics. Strong correlations were observed with FloX (R² = 0.93, 0.92 for workflows 1 and 2b, respectively), indicating strong agreement between both sensors, while moderate correlations with HyPlant (R² = 0.56, 0.52), suggest a reasonable cross-sensor consistency, likely influenced by the differences in spatial resolution and retrieval methods.
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